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Stirrers and mixers are highly used in chemical, food, pharmaceutical, cosmetic, concrete industries and others. During the fabrication process, the equipment may fail to appropriately stir or mix the solution. Besides that, it is also important to determine when the right homogeneity of the mixture is attained. Thus, it is very important to have a diagnosis tool for these industrial units to assure the quality of the product and maintain market competitiveness. Nuclear techniques, such as gamma densitometry, are widely used in industry to overcome a sort of difficulties, as they are minimally non-invasive techniques. This paper presents a method based on the principles of the radioactive particle tracking technique to predict the instantaneous position of a radioactive particle to monitor a concrete mixture inside an industrial unit by means of Monte Carlo method and artificial neural network. Counts obtained by an array of detectors properly positioned around the mixing canister will be correlated to each other, by means of an appropriate mathematical search location algorithm, in order to predict the instantaneous positions occupied by an inserted radioactive particle. The simulation consists of a detection geometry of eight NaI(Tl) scintillator detectors, a 662 keV 137Cs point source with isotropic emission of gamma-rays and a polyvinyl chloride tank. At first, the tank is air filled and, afterwards, filled with concrete made with Portland cement. The modeling of the detection system is performed using the MCNPX code. For both medium, the correlation coefficient was 0.99 for all coordinates, which indicates that this methodology could be a good tool to evaluate industrial mixers. • Radioactive Particle Tracking methodology developed using MCNPX code. • The detection system uses 137Cs (662 keV) gamma-ray source and eight NaI(Tl) detectors. • An artificial neural network gives the position of the radioactive particle. [ABSTRACT FROM AUTHOR]

In Brazil, concrete and cement are highly used in construction, therefore mixers are widely used in this industry. During the fabrication process of concrete/cement, the equipment may fail and compromise the appropriate mixing procedure. Besides that, it is also important to determine the right point of homogeneity of the mixture. It is important to have a methodology to monitor the mixing process to ensure the quality of the product. This study presents a methodology based on the principles of the radioactive particle tracking technique to predict the instantaneous positions occupied by the radioactive particle inside an industrial mixer by means of a mathematical location algorithm. The detection geometry modeled by means of MCNPX code employs an array of eight NaI(Tl) scintillator detectors, a 198Au spherical gamma-rays source with isotropic emission and a test section filled with concrete that represents an industrial mixer. The choice of the radionuclide is due its well-characterized peak of 411 keV, its half-life of 2.7 days and the possibility to obtain 198Au by neutron activation in reactors. The purpose of this study is to use an artificial neural network as a location algorithm of the 198Au radioactive particle inside an industrial mixer. Results showed that over 56% of the cases were below 5% of relative error for all coordinates of the radioactive particle, which indicates that it is possible to track the radioactive particle trajectory inside the industrial mixer using the artificial neural network algorithm. • Radioactive Particle Tracking methodology to evaluate concrete mixer. • Mathematical simulation developed using MCNPX code. • Geometry employs 198Au (411 keV) gamma-ray source and eight NaI(Tl) detectors. • An artificial neural network calculates the radioactive particle position. [ABSTRACT FROM AUTHOR]

In Brazil, concrete and cement are highly used in construction, therefore mixers are widely used in this industry. During the fabrication process of concrete/cement, the equipment may fail and compromise the appropriate mixing procedure. Besides that, it is also important to determine the right point of homogeneity of the mixture. It is important to have a methodology to monitor the mixing process to ensure the quality of the product. This study presents a methodology based on the principles of the radioactive particle tracking technique to predict the instantaneous positions occupied by the radioactive particle inside an industrial mixer by means of a mathematical location algorithm. The detection geometry modeled by means of MCNPX code employs an array of eight NaI(Tl) scintillator detectors, a 198Au spherical gamma-rays source with isotropic emission and a test section filled with concrete that represents an industrial mixer. The choice of the radionuclide is due its well-characterized peak of 411 keV, its half-life of 2.7 days and the possibility to obtain 198Au by neutron activation in reactors. The purpose of this study is to use an artificial neural network as a location algorithm of the 198Au radioactive particle inside an industrial mixer. Results showed that over 56% of the cases were below 5% of relative error for all coordinates of the radioactive particle, which indicates that it is possible to track the radioactive particle trajectory inside the industrial mixer using the artificial neural network algorithm. • Radioactive Particle Tracking methodology to evaluate concrete mixer. • Mathematical simulation developed using MCNPX code. • Geometry employs 198Au (411 keV) gamma-ray source and eight NaI(Tl) detectors. • An artificial neural network calculates the radioactive particle position. [ABSTRACT FROM AUTHOR]